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A user dependent multi-resolution approach for biometric data

机译:基于用户的生物特征数据多分辨率方法

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This paper focuses on the use of a user dependent multi-resolution approach based on local ternary pattern (LTP) in biometric verification. Following an extensive review of the literature on texture descriptors, several methods are compared on well known biometric problems: palm verification and knuckle verification. We propose approaches for extracting a set of local ternary pattern bins separately from the training set of each user, then the Chi square distance is used to compare two templates. The paper is more experimental than novelty in algorithm, our aim is to compare our system with the standard multi-resolution approach, with the novel hierarchical local binary patterns (HLBP) and with different fusions. Extensive experiments conducted over the two well-known biometric characteristics (palmprint and knuckleprint) show the strength of our approach. When each user is given the related selected bins, a near 0 equal error rate is obtained. When the impostor steals the 'selected bins' of the user that he claims to be, our approach slightly outperforms both the standard multi-resolution approach and HLBP. A further improvement in the performance is obtained combining LTP and HLBP.
机译:本文着重在基于生物特征的验证中使用基于本地三元模式(LTP)的依赖用户的多分辨率方法。在对有关纹理描述符的文献进行了广泛回顾之后,针对众所周知的生物特征问题对几种方法进行了比较:手掌验证和指关节验证。我们提出了从每个用户的训练集中分别提取一组本地三元模式仓的方法,然后使用卡方距离来比较两个模板。本文在算法上比新颖性更具实验性,我们的目的是将我们的系统与标准多分辨率方法,新颖的分层局部二进制模式(HLBP)和不同融合进行比较。在两个众所周知的生物特征(掌纹和指节纹)上进行的大量实验表明了我们方法的优势。当为每个用户提供相关的选定仓位时,将获得接近于0的相等错误率。当冒名顶替者窃取他声称是用户的“选定垃圾箱”时,我们的方法在性能上比标准的多分辨率方法和HLBP稍好。结合使用LTP和HLBP,可以进一步提高性能。

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